The rStar2-Agent framework boosts a 14B model to outperform a 671B giant, offering a path to state-of-the-art AI without ...

Abstract: Simulation-based optimization is a widely used method to solve stochastic optimization problems. This method aims to identify an optimal solution by maximizing the expected value of the ...

Abstract: Real-world production scenarios often involve multiobjective optimization problems with intricate constraints. Although there has been a growing interest in multiobjective problems with ...

This is the official implementation of our ICLR 2025 paper "UniCO: On Unified Combinatorial Optimization via Problem Reduction to Matrix-Encoded General TSP". Fig 1. The 3-step workflow of the UniCO ...

In the Florida Everglades, authorities are using robotic rabbits to combat the invasive Burmese python population. These bunnies, designed to mimic real rabbits, attract pythons out of hiding, aiding ...

Florida faces a growing problem with Burmese pythons, as more than 23,000 of these non-native snakes have been removed from the state since 2000, according to the Florida Fish and Wildlife ...

A line of engineering research seeks to develop computers that can tackle a class of challenges called combinatorial optimization problems. These are common in real-world applications such as ...

Adaptive Optimization Framework for AI Agents using Reinforcement Learning. Automatically optimize agent behaviors, reduce costs by 30%, and improve performance through Q-learning and data-driven ...

In this video, we implement the Adam optimization algorithm from scratch using pure Python. You'll learn how Adam combines the benefits of momentum and RMSProp, and how it updates weights efficiently ...